The prediction of faulting and fracturing is very important in oil and gas exploration and production. The reservoirs in which hydrocarbons have been located include clastics (sandstones etc.) and carbonates (limestones etc). Faulting has general value for both types of rocks, but distribution of fracture in carbonates is a particular target of the development of oil and gas fields.
Traditional techniques use the time, amplitude and velocity attributes of reflected waves to recognize stratigraphic features and sudden changes of attributes interpreted as faulting. The methods which use correlation coefficients between adjacent seismic traces, derivative difference, amplitude difference, eigenvalues and other statistical parameters derived from adjacent seismic traces require time consuming, manual interpretation of the geologic faults and fractures.
Automatic methods of fault identification and fracture evaluation use only reflections and suffer from “false alarms” connected with correlation losses specified by influence of the upper part of the subsurface. Methods exist for separation of the wave field into diffracted and reflected waves, dependent on artifacts specified by Radon-transform and on exactness of velocity analysis. Diffracted images are performed without taking into account the angle of incidence for different types of waves.
Traditionally, subsurface formations are analyzed using reflection data that is processed into image interfaces with impedance contrast, but seismic data contain two types of coherent events generated from subsurface discontinuities: reflections and diffractions. Diffractions are generated by local discontinuities, which act as a point source, whereas reflections are generated by an extensive reflection boundary. Faulting of stratigraphic subterranean formations creates hydrocarbon traps and flow channels. Therefore, accurate identification of the faults is essential to the interpretation of seismic data.
In many geological basins the detailed identification of faults and degree of fracturing can be extremely useful in seismic reservoir characterization. Changes in the elastic properties of subsurface rocks appear as seismic reflections. Such changes in the properties of the rocks typically occur at boundaries between geologic formations, at fractures and at faults.
Faults may be detected by looking at vertical displacement of seismic reflectors in 2-D and 3-D data. The continuity of seismic reflectors in seismic amplitude data may be quantified by computing the correlation coefficient between adjacent seismic traces over a movable vertical window (Bahorich et al, U.S. Pat. No. 5,563,949), derivative difference (Luo, et al., U.S. Pat. No. 5,724,309), amplitude difference (Luo et al., U.S. Pat. No. 5,831,935) and eigenvalues derived from adjacent seismic traces (Gersztenkorn, et al, U.S. Pat. No. 5,892,732). However, each of these methodologies requires the manual interpretation of the geologic faults, which is time consuming.
Some methods have been proposed for automatic picking of faults. Crawford, et al, in U.S. Pat. No. 5,987,388 describes such a method, which includes processing of individual lateral slices of the 3-D seismic volume, wherein for each lateral slice, stripe artifacts are eliminated by adjusting pixel values to account for lines that are unduly bright or dim. Detection of lines in the enhanced lateral slice is then performed by summing pixel intensities over a window at varying directions.
Neff, et al, in U.S. Pat. No. 6,018,498, uses test planes, which are mathematically inserted into the seismic data volume to approximate dip and azimuth of a potential fault plane surface. Goff, et al, in U.S. Pat. No. 7,069,149, describes a method for extracting geologic faults from seismic data, which includes calculating a minimum path value for each voxel of an attribute cube, extracting a fault network skeleton by utilizing the minimum path values corresponding to voxels, flood filling the fault network to identify a plurality of fault segments, labeling the fault segments and creating a vector description of the fault network skeleton.
In all these methods only reflected waves are used. The reflected wave field may be aggravated by local places of noise and zones of correlation losses specified by the influence of the upper part of the subsurface. These features frequently have properties of a fault and look like a fault. This may lead to identification of false disturbances interpreted as faults. Therefore, it is necessary to separate independent wave types, namely diffracted and reflected waves, in order to analyze them independently.
A method for separation of diffraction and reflection waves on seismic data is proposed by Taner, et al, “Separation and imaging of seismic diffraction using plane-wave decomposition,” SEG/New Orleans 2006 Annual Meeting. The plane wave decomposition of split spread common shot records is used in this method. Plane-wave destruction filtering is applied with subsequent velocity analysis and a prestack migration procedure. Deficiencies of this method consist in its dependence on artifacts connected with a Radon-transform and exactness of velocity analysis.
A method for detection of diffracted waves by concentrating the signal amplitudes from diffracting points on the seismic section is proposed by Landa, et al, “Seismic monitoring of diffracted images for detection of local heterogeneities,” Geophysics, 63, p. 1093. The difference in properties of the reflected and diffracted waves connected with the angle of incidence of the wave to the surface is not considered in this method.
In general, interference, multiple reflections or artifacts introduce regular and irregular noise, which sometimes renders the image of the subsurface difficult to interpret. CMP stacking is used for suppression of noise and multiples, but it does not give the desired result in the case of small folding. A multifocusing method (Berkovitch, et al, “Basic formulae for multifocusing stack,” 56th Mtg. Eur. Assoc. Expl Geophys., 1994, Extended Abstracts, Session, p. 140) makes it possible to increase the signal to noise ratio in cases of small folding. The multifocusing (MF) method is based on the Homeomorphic imaging method, by Gelchinsky, described in U.S. Pat. No. 5,103,429. MF consists of stacking, which involves all available traces around the central trace.
The coherency of stacking of useful events is achieved by an adaptive moveout correction procedure based on the parameterization of wave fronts by oblique spherical arcs and by dynamic ray tracing. The MF stack represents the optimal stacked values, corresponding to the optimal parameters (β—angle of incidence of the wave front, R— the radius of curvature of the wave front, connected with velocity in the media and R*- the radius of the circle arc, which approximates a segment of the reflector), and is close to an accurate zero-offset section.
The exact knowledge of all three parameters allows calculation of the exact NMO corrections within the base of the analysis. For definition of exact values of these parameters the scanning of them with a constant increment is performed with calculation of the coherency measure. In particular, scanning the parameters β and R leads to the formation of the cube S(β,R,t), where t is the time, filled with semblance values, for each central trace of the image. The maximum of the coherency measure corresponds to optimal values of β, R and R* on each time sample.
A core and unique characteristic of MF is its ability to pick out the diffracting objects by focusing the diffracted waves.
Thus it would be desirable to provide an improved method for analyzing the diffraction and reflection of waves derived from seismic data.